The enjoyment or dislike of particular goods and services is a subjective judgment made by individuals based on any number of criteria. The ability to make reliable recommendations to a particular person for a given item, such as a movie for example, would be useful. Such information would enable someone to avoid such items that would not be enjoyable and choose such items as would be pleasing.
There is often information of how well received an item is with respect to the general population, such as a popular movie or book. However, such information does not provide a means to determine to what extent or even if a particular individual will like the movie, or book. Such generalized information only provides the likelihood that a randomly selected individual will appreciate a particular item such as a given movie.
There are many critics that rate movies and the like. An individual can try to identify such a critic with preferences that are at least somewhat similar after much experimentation, time and money spent viewing movies. However, such a process is neither reliable nor repeatable on a regular basis and does not take into account particular likes and dislikes.
There exists, however, a class of systems referred to as automated collaborative filtering (ACF) systems which provide recommendations to a user based on ratings of items by other system users. ACF systems include the entry and storage of ratings data by users of the system to generate customized ratings. The ratings of items stored in the system reflect an individual user's personal tastes. An ACF system attempts to find users with similar preferences as a particular user and provide recommendations to that user based on those similar preferences.
As mentioned above, prior art ACF systems have attempted to provide recommendations to a user based on ratings for items provided by the user as compared with other users. See for example U.S. Pat. No. 4,996,642 to Hey and 1994 MIT Media Lab Master's Degree thesis of Upendra Shardanand. However, these recommendation systems fail to take into account the probability that a random user will provide a given rating. Thus, information showing unusual similarity in preferences for particular users is not utilized. Furthermore, these prior art systems also do not provide recommendations with statistically meaningful confidence levels as the number of items that both the user and a respective recommending user have provided ratings for increases.